{"id":"W2076267319","doi":"10.1021/pr049784w","title":"Quantitative Proteome Analysis Using Differential Stable Isotopic Labeling and Microbore LC−MALDI MS and MS/MS","year":2005,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Heritage Foundation for Medical Research","keywords":"Chemistry; Mass spectrometry; Chromatography; Proteome; Peptide; Protein mass spectrometry; Matrix-assisted laser desorption/ionization; Bottom-up proteomics; Isobaric labeling; Sample preparation; Tandem mass spectrometry; Quantitative proteomics; Mass spectrum; Proteomics; Analytical Chemistry (journal); Desorption; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001125618,0.001034249,0.0007459205,0.0009730235,0.0003508865,0.0007347587,0.0008793037,0.0006105731,0.000801359],"category_scores_gemma":[0.0004587937,0.0002524023,0.00034957,0.0006403237,0.0005018004,0.0006529976,0.0005669597,0.0008624442,0.0004619677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004938351,"about_ca_system_score_gemma":0.0004348889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003164233,"about_ca_topic_score_gemma":0.000693051,"domain_scores_codex":[0.9991297,0.0001172954,0.00004975234,0.0002617396,0.0003795019,0.00006205351],"domain_scores_gemma":[0.9997336,0.00006916239,0.00005572979,0.00003758336,0.00007375847,0.00003003943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002751909,0.00002750065,0.0001014697,0.00005652795,0.000008439819,0.0000204981,0.000006196955,0.00009130056,0.9931484,0.0002309452,0.00006730983,0.006213973],"study_design_scores_gemma":[0.000008151828,0.0001192949,0.0007435109,0.000004607061,0.00001671221,0.000146039,0.000006434885,0.004918701,0.9904517,0.0002289808,0.003342686,0.00001322128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2451836,0.004683136,0.7420884,0.000326034,0.0002060222,0.0004807262,0.001651115,0.002516973,0.002864075],"genre_scores_gemma":[0.2002581,0.002702606,0.7915353,0.0003051636,0.00007742396,0.0007403424,0.001393999,0.0001570576,0.002830037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001125618,"threshold_uncertainty_score":0.005952895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07419963101979488,"score_gpt":0.3965297546361977,"score_spread":0.3223301236164028,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}